How Machine Learning Might Offer Opportunities to Preempt Escalations

Traditional customer support escalation processes focus on mitigating vs. avoiding user dissatisfaction. This often adds to the customer’s already-present frustration; lowers service satisfaction ratings; and increases support costs by requiring intervention from senior staff and management. We believe that Machine Learning (ML) can help support organizations shift the paradigm from reactive to proactive by systematically predicting escalation triggers and driving interventions before customers get frustrated — improving the service experience, lowering costs, and increasing staff efficiency along the way. This article examines the author’s experience in building an ML tool to predict service escalations and describes key takeaways from his journey.

By Sameer Patkar, VP – Oracle Support Services

Business Problem

Escalations comprise only a small percentage of most support teams’ overall ticket volumes. While proportionally few, each escalated incident incurs a higher transnational cost to resolve by requiring additional time from managers and technical resources for monitoring, follow-up, and customer communication. Preempting escalations can therefore help support organizations produce better outcomes for their customers and themselves.

To that end, support teams have utilized various approaches in the past to try and identify tickets that might escalate; most have proven to be partial solutions that either did not scale or could not reliably predict triggers. For example, one effective way to avoid escalations that occur due to lack of timely response is for ticket owners to seek help from others if they are stuck on how to proceed with an incident. While some do, most support engineers prefer to resolve problems themselves rather than seek help. Consequently, organizations have enacted external review and monitoring processes wherein managers and/or peers review open within another team member’s queue. This approach is time-consuming and does not scale.

Another popular approach is to use rules-based reports that run on all open tickets in backlog and apply logic meant to identify markers of missed expectations. While more scalable than manual audits, rules-based reports tend to identify too many potential escalations to be used reliably; the volume of “false positives” they produce can quickly inundate support personnel with additional work that may or may not legitimately require investigation.

This occurs because rules have limitations: they must be written to know about ALL potential conditions that could lead to escalation and they must have data available on which to operate. Conversely, customer dissatisfiers are not always explicitly stated in ticket data, but must instead be derived forensically by examining each individual interaction from the inception of the ticket to identify missed expectations. The relationship between this derived data and the reasons why customers escalate can be complex and not reducible to simple rules. Furthermore, customers express dissatisfaction in text updates that also convey their sentiment. Rules-based approaches are not very effective at assessing text-based sentiment and then correlating that sentiment with derived data. Finally, relationships across data elements can change over time, which can require rule reprogramming.

This article is an excerpt from a longer article Sameer wrote for the ASP.  To read the rest of this article you must be logged as an ASP Member.


Please Login:

Not an ASP Member? Learn about the benefits of joining the Association of Support Professionals
Learn More about the ASP

Related Articles

Resideo – 2019 ASP Top Ten Winner Profile

Resideo is a 2019 Top Ten winner in our large company group. This is their first time in the Top Ten and they are to be commended for doing so well. That said, it is apparent that they still have a ways to go to catch up to some of the more elite companies, such as Red Hat. This is a very good start, however and a great achievement for them. We look forward to seeing how they continue to improve their site next year. Learn how the Resideo team has developed its most recent web support capabilities and been recognized by the ASP for their web support efforts.

Red Hat – 2019 ASP Top Ten Winner Profile

RedHat is a 2019 Top Ten winner in the large-sized company group. This was their ninth win in a row. They are obviously doing many things right. RedHat’s many wins highlight their continuing site improvement efforts. They appear to understand user expectations for support continue to change and they must continue their efforts to improve all aspects of their self-service site and its use, such as through Google. This company does not rest on their laurels, but works hard to continue to evolve their site. Learn how the Red Hat team has developed its most recent web support capabilities and been recognized nine times by the ASP for their web support efforts.

NetApp – 2019 ASP Top Ten Winner Profile

NetApp is a 2019 Top Ten winner in the large-sized company group. This was their second win in the past two years. Learn how the NetApp team has developed its most recent web support capabilities.

Quest – 2019 ASP Top Ten Winner Profile

Quest is a 2019 Top Ten winner in the medium-sized company group. Learn how the Quest team has developed its most recent web support capabilities and been recognized five times by the ASP for their web support efforts (as Quest Software and as part of Dell). This report includes Top Ten Judges Comments (new for 2019) and the winning essay submitted by the Quest team.